A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.

Automated summary

The study introduces a detection method that adapts anchoring based on sample balance, selecting candidate anchors through horizontal IoU and using an adaptive threshold module to maintain a balance between positive and negative anchors, ultimately achieving better performance on a public aerial image dataset.